Data items stored in large scale systems often include significant amounts of data and metadata (e.g., data about data). However, some data may be incomplete or missing in the large scale systems due to faulty data source interfaces or human mistakes in data entry. Existing systems do not provide simple, efficient, and intuitive ways of identifying missing data and correctly enriching the missing data with correct data. Additionally, while metadata can provide valuable structures for efficient data organization and analysis, missing metadata may also go undetected due to its associative nature in relation to the original data. The problem is especially acute in systems managing large data sets. Without proper detection and subsequent enrichment of data and metadata, the missing data and metadata can cause disruption in data organization and analysis efforts.
Although it is desirable for users to have systems and techniques for easy detection and effective management of missing data and metadata, existing systems may not provide such effective detection, visualization, and/or management of data and metadata. For example, existing systems may not readily bring the missing data or metadata to attention, and may only provide limited information from which to identify the missing data and metadata. Even when the missing data or metadata is identified, the existing systems may not provide intuitive and efficient ways of determining the correct data or metadata to enrich the missing data or metadata. Additionally, such systems may be inflexible in management of data and metadata, and may not provide intuitive reporting of the data and metadata.